MLflow

Capture complete traces of your LLM applications

This feature allows users to capture comprehensive traces of their LLM applications and agents, providing deep insights into their behavior. Built on OpenTelemetry, it supports any LLM provider and agent framework, enabling users to monitor production quality, costs, and safety effectively.

Run systematic evaluations

Users can run systematic evaluations to track quality metrics over time and catch regressions before they reach production. The feature offers over 50 built-in metrics and LLM judges, with the flexibility to define custom metrics using APIs, ensuring comprehensive quality assurance.

Automatically detect issues in your traces

This feature utilizes AI-powered analysis to automatically detect issues in traces across various dimensions such as correctness, latency, execution, adherence, relevance, and safety. It helps users maintain high standards of performance and reliability in their applications.

Version, test, and deploy prompts

Users can version, test, and deploy prompts with full lineage tracking, ensuring that all changes are documented and traceable. This feature enhances the management of prompt iterations and facilitates better performance through systematic testing.

Automatically optimize prompts

This feature employs state-of-the-art algorithms to automatically optimize prompts, improving their performance without requiring extensive manual intervention. It streamlines the process of enhancing prompt effectiveness, making it easier for users to achieve desired outcomes.

Unified API gateway for all LLM providers

The AI Gateway provides a unified API for all LLM providers, allowing users to route requests, manage rate limits, handle fallbacks, and control costs through a single OpenAI-compatible interface. This simplifies the integration process and enhances operational efficiency.

Deploy agents to production with a single command

The MLflow Agent Server allows users to deploy agents to production quickly and efficiently with a single command. It features a FastAPI-based hosting solution that includes automatic request validation, streaming support, and built-in tracing, facilitating a smooth transition from prototype to production.

Manage the full machine learning lifecycle

MLflow enables users to manage the entire machine learning and deep learning model lifecycle, including experiment tracking and hyperparameter tuning. This comprehensive approach helps teams streamline their workflows and improve model performance.

Experiment tracking

This feature allows users to track experiments systematically, capturing parameters, metrics, and artifacts. It provides a clear overview of different runs, facilitating better decision-making and optimization of model performance.

Model evaluation capabilities

MLflow provides robust model evaluation capabilities, enabling users to assess model performance against various metrics. This ensures that models meet the required standards before deployment, enhancing reliability and effectiveness.

Production model registry

The production model registry allows users to manage and version models effectively, ensuring that the right models are deployed in production. This feature supports collaboration and governance in model management.

Model deployment tools

MLflow offers tools for deploying models into production environments seamlessly. This feature simplifies the deployment process, allowing teams to focus on building and improving their models rather than managing deployment logistics.